This paper investigates how social factors predict attitude toward gamification and intention to continue using gamified services, as well as intention to recommend gamified services. The paper employs structural equation modelling for analyses of data (n=107) gathered through a survey that was conducted among users of one of the world’s largest gamification applications for physical exercise. The results indicate that social factors are strong predictors for attitudes towards gamification, and, further, continued use intentions and intentions to recommend the related service.

Social Motivations To Use Gamification: An Empirical Study Of Gamifying Exercise

A Genetic-Neuro-Fuzzy inferential model for diagnosis of tuberculosis

Formulation of qualitative models for complex decision problems exhibiting less structure, more imprecision and uncertainty is not adequately addressed in DSS research Typical characteristics and requirements of such problems prohibit the development of DSS using knowledge based system development methodologies This paper presents a methodology for formulation of qualitative models using fuzzy logic to handle the imprecision and uncertainty in the problem domain The problem domain, in this methodology, is represented using problem-solving knowledge, environmental knowledge, and control knowledge components A high level non-procedural language for representing these components of knowledge is illustrated using a project selection and resource allocation problem The paper also describes the implementation of a prototype decision support environment based on this methodology

Formulation of qualitative models using fuzzy logic

House hunting meaning the activity of trying to find a house to live in, is considered as one of the most important activities for many families worldwide. This involves many criterions/factors to be measured and evaluated. These factors are expressed both in quantitative and qualitative ways. In addition, a hierarchical relationship exists among the factors. Moreover, it is difficult to measure qualitative factors in an objective/quantitative way, resulting incompleteness in data and hence, uncertainty. Therefore, it is necessary to address the issue of uncertainty by using appropriate methodology; otherwise, the decision to select a house to live in will become inappropriate. There exist many methods such as Analytical Hierarchical Process (AHP), Analytical Network Process (ANP), Inner Product Vector (IPV) to address the issue presented in this paper. However, none of them is able to address the issue of uncertainty and hence, resulting inappropriate selection of a house to live in. Therefore, this paper demonstrates the application of a novel method named Evidential Reasoning (ER), which is capable of addressing the uncertainty of multi-criterion problem, where there exist factors of both subjective and quantitative nature. The ER approach handles uncertainties by using a belief structure, the evidential reasoning approach used in aggregating degrees of belief from lower level attributes to higher level attributes [7]. This paper reports the development of DSS using ER approach, which is capable of providing overall assessment on the location of a house to live in taking account of both qualitative and quantitative factors. Chittagong, which is the second largest city of Bangladesh has been considered as the case study area to demonstrate the application of the developed DSS..

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An Evidential Reasoning-based Decision Support System to Support House Hunting

One of the goals of Smart Grids is to encourage distributed generation of energy in houses, hence allowing the user to profit by injecting energy into the power grid. The implementation of a differentiated tariff of energy per time of use, coupled with energy storage in batteries, enables profit maximization by the user, who can choose to sell or store the energy generated whenever it is convenient. This paper proposes a solution to the sequential decision-making problem of energy sale by applying reinforcement learning. Results show a significant increase in the total long-term profit by using the policy obtained with the proposed approach, when compared with a price-unaware selling policy.

Paul Waller

Papers

Expert Systems and Artificial Intelligence in Decision Support Systems